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2nd Asia Conference on Electronics engineering, ACEE 2024

作者： Yuan, Ruijian Zhang, Yongli Geng, Lihui Li, Xuehui Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin China

ISBN: (纸本)9798350375251

The two-wheeled bicycle robot (TWBR) is a new intelligent transportation, which can be used for special tasks such as forest operation and disaster relief. Although the TWBR is not structurally complex, the dynamics are very complex to model. It is extremely difficult to build an accurate mathematical model. Wind disturbance is, in principle, always present on open roads and influence on the driving stability increases. Aiming at mathematical modeling error caused by mass center of the TWBR frame and external disturbance caused by wind force, this paper introduces a suitable control method. Firstly, the Kane method is adopted for modeling. Secondly, robust control can provide stability and performance guarantee for both uncertainty and disturbance. Finally, the results of simulation and physical experiment show the effectiveness of the robust H∞ control for TWBR. In this paper, MATLAB/Simulink is used to establish a combined mathematical model of wind force as an experimental external disturbance. The robust H∞ controller based on linear matrix inequality (LMI) and Linear quadratic regulator(LQR) are realized. It is verified that the robust H∞ controller has better effectiveness than the LQR controller when facing the uncertainty and external disturbance. © 2024 IEEE.

关键词： Bicycles

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Parameter Identification and control of Two-Wheel Balance Vehicle Model Based on Subspace Algorithm 2

Parameter Identification and Control of Two-Wheel Balance Ve...

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2nd Asia Conference on Electronics engineering, ACEE 2024

作者： Li, Xuehui Zhang, Yongli Geng, Lihui Yuan, Ruijian Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin China

ISBN: (纸本)9798350375251

To address parameter uncertainty in the two-wheel balance vehicle (TWBV) system, a closed-loop subspace algorithm (CLSA) was employed to identify and correct uncertain structural parameters. This got in a more accurate system model. Based on the dynamics mechanism model of the TWBV, we designed a simple controller to achieve self-balancing control. We harvested the input and output sample data required for identification. The method used to filter out noise from the original signal and extract essential dynamic characteristics of the system is the input-output bias voltage function. The parameters of the dynamics model for the TWBV were subsequently identified and corrected using the CLSA. Finally, the linear quadratic regulator (LQR) controller is designed based on the model obtained from identification and compared with the original unidentified model. The results of the simulation and physical experiment show that the control system with the modified system after identification has better performance. This paper verifies the effectiveness of the proposed control strategy, which provides a reference scheme for solving model uncertainty in similar systems and improving control accuracy. © 2024 IEEE.

关键词： Vehicle wheels

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Optimal Multi-agent Collision-Free Path Planning with Temporal Logic Constraints 5th

Optimal Multi-agent Collision-Free Path Planning with Tempo...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Zheng, Yiwei Liu, Zhaocong Lai, Aiwen Yu, Xiao Lan, Weiyao Department of Automation Xiamen University Fujian Xiamen361005 China The Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100094 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9789811939976

In this paper, we present a novel optimal multi-agent collision-free path planning algorithm (OMPATL) for the problem of multi-robot path planning with temporal logic constraints. Given a high-level mission specification in the form of linear temporal logic (LTL), OMPATL can automatically synthesize a set of trajectories for the multi-agent system (MAS), which achieves the collision avoidance among agents and minimizes the cost of the maximum traveling time between successive satisfactions. To synthesize the optimal trajectories, the motion of all agents, together with the mission area, is modeled as a weighted team transition system (TS) with a set of atomic propositions (APs). If the collision avoidance were not required, the optimal trajectories would be easily synthesized by an algorithm such as shortest paths. However, once the multi-agent systems are required to be safety-critical and collision-free, these trajectories may become infeasible. In OMPATL, a strategy is proposed for adding a set of transitions including "wait" and "go-back" to each individual TS based on the optimal run which does not consider collision avoidance. Then, the states and transitions with collisions can be removed such that the optimal collision-free trajectories are founded by the synthesis algorithm. The synthesized run balances collision avoidance, mission fulfillment, and optimality. The optimal run is synthesized offline and online adjustments are not needed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Attack Detecting for the Multi-robots Systems: An Integrated Cyber-Physical Approach 7th

Attack Detecting for the Multi-robots Systems: An Integrat...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Yang, Chunyu Xu, Shanguang Ma, Lei Wu, Tihao Ye, Hongmei Xu, Yiming School of Information and Control Engineering China University of Mining and Technology Xu’zhou221116 China Jiangsu Institute of Automation Lianyungang China

ISBN: (纸本)9789819733316

To address security issues and effectively detect cyber-attack behaviors in multi-robot systems, this paper proposes a cyber-attack detection method that combines information-physical features. The method involves performing cyber intrusions, such as false data injection attack, replay attack, and DoS attack, on a multi-robot system using the Kali Linux operating system to disrupt its collaborative motion process. Physical state information of the system and network traffic information are then collected. Based on Convolutional Neural Network (CNN), a cyber-attack detection method is designed that fuses the information-physical features. The proposed method is validated on a multi-robot system in the Gazebo simulation environment. Simulation and experimental results demonstrate that the proposed cyber-attack detection method can effectively detect system anomalies with higher accuracy compared to the detection methods based solely on physical features or network traffic characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multipurpose robots

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Collision-free and Effecient Robotic Grasping based on Hierarchical Reinforcement Learning

Collision-free and Effecient Robotic Grasping based on Hiera...

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2024 Jiangsu Annual Conference on automation, JACA 2024

作者： Li, Yi Fang, Fang Liu, Chenyang Wang, Yutao Fan, Tao School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9781837242689

In the realm of robotics, grasping within unstructured environments has consistently been a challenging task. Direct grasping often results in failures due to collisions, and some pre-grasping maneuvers can eliminate the tightly stacked state between objects, thereby enhancing the success rate of grasping. In this paper, we opt for pushing as a pre-grasping maneuver. We propose a deep Q-network with a fused convolutional feature enhancement module to improve action value estimation, enhancing the robot’s pushing and grasping abilities. Additionally, a hierarchical reinforcement learning framework optimizes action sequences for better synergy between pushing and grasping. In simulated experiments, the model achieved a high grasp success rate with just a few hours of training, outperforming other methods in the literature. © The Institution of engineering & Technology 2024.

关键词： Adversarial machine learning

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Doppler Measurement-Based Positioning and Tracking Methods with External Radiation Radar 2

Doppler Measurement-Based Positioning and Tracking Methods w...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Liu, Weifeng He, Linqing Tian, Zhengwang Shaanxi University of Science and Technology School of Electrical and Control Engineering Xi'an China Hangzhou Dianzi University School of Automation Hangzhou China

ISBN: (纸本)9798331515669

This paper introduces a new approach for addressing the complex problem of Multi-target tracking in external radiation radar systems equipped with Multi-static Doppler measurements. Drawing from research on sensor arrays and mobile sensors, the method utilizes the Generalized Labelled Multi-Bernoulli (GLMB) filter to navigate the intricacies of Multi-target tracking across sensor arrays. The study validates the effectiveness of the GLMB and the computationally efficient Gibbs-GLMB filters in a setup involving three transmitters and one receiver. Experimental results, assessed with the Optimal Sub-Pattern Assignment (OSPA) metric, suggest that the Gibbs-GLMB filter accelerates computation while potentially compromising on measurement precision, which could lead to reduced accuracy in specific cases. The investigation also examines the influence of sensor geometry on tracking results, indicating that there is an association between tracking performance and sensor arrangement. © 2024 IEEE.

关键词： Radar target recognition

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Online Residual Learning from Offline Experts for Pedestrian Tracking 63

Online Residual Learning from Offline Experts for Pedestrian...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Vlachos, Anastasios Tsiamis, Anastasios Karapetyan, Aren Balta, Efe C. Lygeros, John Eth Zürich Automatic Control Laboratory Department of Information Technology and Electrical Engineering Zürich8092 Switzerland Control and Automation Group Inspire ZürichAG 8005 Switzerland The Automatic Control Laboratory Switzerland

ISBN: (纸本)9798350316339

In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predictions generated before or at the beginning of the prediction horizon. We augment every offline prediction by learning their respective residual error concerning the true target state online, using the recursive least squares algorithm. At a higher level, we treat the augmented lower-level predictors as experts, adopting the Prediction with Expert Advice framework. We utilize an adaptive softmax weighting scheme to form an aggregate prediction and provide guarantees for ORL in terms of regret. We employ ORL to boost performance in the setting of online pedestrian trajectory prediction. Based on data from the Stanford Drone Dataset, we show that ORL can demonstrate best-of-both-worlds performance. © 2024 IEEE.

关键词： Prediction models

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Design of Load Frequency controller for Power System Based on Equivalent Input Disturbance Method 4

Design of Load Frequency Controller for Power System Based o...

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4th IEEE New Energy and Energy Storage System control Summit Forum, NEESSC 2024

作者： Zhang, Jianrong Xu, Hang Wang, Hongzhang Zhang, Chuanke China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan China

ISBN: (纸本)9798350389012

In the power system, due to the instability of the external environment, safe and stable operation cannot always be guaranteed, as it frequently encounters varying degrees of external disturbances. In order to mitigate the effects of these disturbances on the system, the power system can operate safely and stably, In this paper, a power system load frequency control (LFC) based on Equivalent Input Disturbance (EID) method is proposed from the perspective of active disturbance suppression. Firstly, the disturbances present in the environment are unified as external disturbances, and then a single area the electric power system model with external disturbance was constructed. Then, a controller algorithm based on EID was designed using EID equivalent processing method and linear matrix inequality method to address the issue of external interference in the power system. Finally, a numerical example is used to validate the effectiveness and accuracy of the controller by simulating with two aspects, fixed perturbation and random perturbation, respectively. © 2024 IEEE.

关键词： Linear matrix inequalities

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Structured controller Design for Interconnected Systems via Nonlinear Programming

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1331-1334页

作者： Yanpeng Guan Junpeng Du Xinchun Jia School of Automation and Software Engineering Shanxi UniversityTaiyuan 030006China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China IEEE

Dear Editor,This letter deals with the structural controller design problem of interconnected systems with unknown feedback ***,under a cardinality constraint on the directed communication links among sub-controllers,a distributed controller’s feedback gain and feedback topology are incorporated in a unified co-design ***,the cardinality constraint introduced in the distributed control is represented by a binary integer *** deal with the complementary constraint,a nonlinear programming(NLP)is proposed to relax the binary integer ***,incorporating the NLP into the standard distributed event-triggered control method,an algorithm is developed for interconnected systems to simultaneously design the feedback topology and controller *** interconnected system is composed of several coupling subsystems,which usually coordinate with each other to accomplish a common task.

关键词： constraint integer topology

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